Behavioural Economics & Finance
Lecture 1: Introduction
1.1 Wie-wat-waar-wanneer
BE:
• Focuses on understanding why people make economic choices and tests the standard economic model.
Goes == further than the standard model ==, therefore not neglecting it.
• Integrates insights from psychology, experiments, and social sciences.
• Historical roots trace back to Adam Smith, and later economists.
BF:
• Uses insights from psychology to understand how human behaviour influences the decisions of investors,
markets and managers
• Finance: financial markets, corporate finance: individual decision
• Neoclassical economics: individuals and firms are self interested agents who maximize their utility/profits
in the face of constrained resources
• emerged as a reaction to some market anomalies that traditional theory- Efficient Market Hypothesis (EMH)
& models based on investors rationality-failed to explain
Traditional finance: started by center valuations on rational economic behavior.
Rational economic behavior: is modeled through expected utility maximization
Milestones:
• Adam Smith The theory of moral sentiment (1759)
• Vilfredo Pareto (20th century): Economics shifted focus to rational choice models.
• Herbert Simon (21st century): Introduced bounded rationality – limits in thinking capacity, available
information, and time. People often tend to satisfice rather than to maximize
• Daniel Kahneman & Tversky: Identified cognitive biases, framing effects, and reference points
dependence. => assumptions of the standard economic model
• Nobel Prizes:
o 1978: Herbert Simon for decision-making processes within economic organizations.
o 2002: Kahneman for integrating psychology into economics, especially concerning human judgement
and decision-making under uncertainty.
o 2002: Verlon L. Smith for laboratory experiments as a tool in empirical economic analysis: the study of
alternative market mechanisms.
o 2017: Richard Thaler for contributions to behavioral economics.
1.2 Purpose of this course
Learning outcomes:
• Identify paradoxes in standard economic model and (S)EUT
• Apply behavioral concepts to BehavEconApplications|case studies and applications Analyze individual; time and
social preferences. (preferences)
• Use behavioral insights in policy-making nudges.
• Reflect on real-world applications and research opportunities.
1.3 Standard Economic Model
• Neo-classical economic model: way most economist think about consumer welfare & choice, provides a
unified vision of economy based on some common rationality assumptions
• Assumptions: fully rational agents maximize utility purely driven by their self-intrest
• based on:
o Full information: (external knowledge).
o Stable and known preferences: (internal knowledge)
o Optimal decision-making, people chose best option available: (rational choices)
1
,• Information is not generally available; info about existence of information may also not be available where
info is available, people may not obtain it; they may also not be able to process it if they obtain it
• Systematic deviations from the self-interested rational agent model: exist not only for individuals, but also
for firms
• Agents are generally assumed to maximise their utility
• An agent’s utility is governed by purely selfish concerns, in the narrow sense that it does not take into
consideration the utility of other
• Agents are Bayesian probability operators
• Agents have consistent time preferences according to discounted utility model
• Theories are usually normative AND descriptive at the same time; may lead to ==tensions if they fail
descriptively== normative theories (how people should behave to obtain a certain goal: utility maximisation)
descriptive theories (how people actually behave).
Notation:
- Risk: problems in which probabilities are objectively known, observed.
- Uncertainty: when probability are unknown, not observed
o Probability: number between 0 and 1 that indicated a likelihood that particular outcome will occur
0: impossible & 1: certain
Risk for financial assets: assessed differently (variance, downside risk,…)
The probability of all possible events = 1
• Focus: on binary prospects (lotteries) with 2 outcomes x > y and probability p: (x, p ;y) | 0≤p≤1
• Decision trees: P + (1 – p) = 1
=> interested in preference relations between prospects ~ indicates indifference ≻ strict preference (≠ inequality
(>))
Behavioral Critique:
• Satisficing (H. Simon): People often settle for "good enough" solutions.
• Limited information availability and processing capabilities
• Systematic deviations from rationality occur for individuals and firms
1.4 Expected Value Theory
• Expected Value Theory (EVT): value of a prospect: EV(x, p; y) = px + (1-p)y
• EV of a gamble: the value of each possible outcome times the probability of that outcome for i outcomes: 𝐸𝑉(𝑥𝑖,
𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖
Examples:
1. Suppose you are planning to play at an outdoor concert. The probability of rain tomorrow is 0,30, no rain 0,70.
Suppose you will make €500 if it doesn’t rain, but only €100 if it rains.
EV (0,70)(500) + (0,30)(100) = €380
2. You have the option to participate in a game where:
- with a 50% chance (probability p = 0,5) you win €100.
- with a 30% chance (probability p=0,3) you win €50.
- with a 20% chance (probability p=0,2) you win nothing €0.
EV (0,5 x 100) + (0,3 x 50) + (0,2 x 0) = €65
3. Consider a proposed investment action x considered by an investor.
Buying some shares cheaply at initial issue of IPO, then resell them at higher price. Assume one share costs 30p
Assume there are 3 outcomes:
- Share close at flotation cost : 30p → investor gets nothing (0 p payoff)
2
,- Share close at 60p: → investor gets 30p payoff
- Shares close at 90 → investor gets 60p payoff
Outcomes have equal probabilities of happening: p≈0,33
EV (0,33 x 0) + (0,33 x 30) + (0,33 x 60) ≈ 30p (expected value is in term of payoffs not closing prices!)
=> this is a ‘fair bet’: the cost is equal to the expected value
• = Weighted average of possible outcomes: EV = ∑ p_i * x_i
• Limitation of EVT: St. Petersburg paradox: solution infra
1.5 Expected Utility Theory (EUT)
Faced with uncertainty, an individual would maximize the expected utility across possible state of the world
→ applicable to individual/investor decision making
Difficult to apply: asset pricing framework provides a framework to quantity the tradeoff between risk and
return => portfolio framework
St. Petersburg Paradox
• Coin flip game with infinite expected value contradicts real-world willingness to pay.
=> discrepancy between what people seem willing to pay to endter the game & the infinite expected value
• Daniel Bernoulli (1738): Introduced Expected Utility Theory (EUT) as a solution.
• The determination of the value of an item must not be based on the price, but on the utility it yields
𝐸𝑈(𝑥𝑖, 𝑝𝑖)= σ𝑖 𝑝𝑖𝑈(𝑥𝑖)
Where the assumption is that U’(x)>0 and U”(x) < 0 → utility increases in outcomes (money), but at a decreasing rate
Utility:
- Utility refers to satisfaction or pleasure a person derives from consuming a good, service, level of wealth
- We can use it to order person’s preferences over a set of options numerically by assigning larger numerical
values to more preferred options.
- If you prefer eating apples (A) to eating chocolate (C) => U(A) = 2 & U(C) = 1
Choice:
- Under the standard economic model choice is assumed to be revealed preference
- Given the alternatives X and Y if you choose X then this ‘reveals’ that you prefer X to Y (X>Y)
We use utility to explain how individuals make choices to maximize their overall happiness or satisfaction based
on their preferences.
Decreasing marginal utility: utiliy increases as consumption increases but at a diminishing rate
𝜕𝑈
=> quantifies the added satisfaction that an agent gets from consuming additional units of goods or services U’ x =
𝜕𝑋
EUT model
• Focuses on utility (U) instead of monetary Value. EU = ∑ p_i * U (x_i)
• Diminishing marginal utility: => risk aversion; concave utility functions → Bernoulli proposed that
U(x) = ln(x) would solve the St. Petersburg paradox
The expected utility of prospect xi is given by 𝐸𝑈(𝑥𝑖, 𝑝𝑖)= σ𝑖
𝑝𝑖𝑈(𝑥𝑖)
Note the difference with expected value 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖
- EUT: normal model of how a rational agent should make decisions and not a descriptive model of how
decision are made.
When does expected utility work?
EUT is based on 4 main axioms:
3
, - Completeness: a decision maker has defined preference can always decide; x ≽ y or x ≼ y ∀ x,y. (where x,y
could be prospects)
- Transitivity: a decision maker’s preferences are consistent; if x ≽ y and y ≽ z then x ≽ z.
- Continuity: For prospects x, y and z with x ≽ y ≽ z, then there exists a probability p where y is equally good
as px + (1 − p)z
Formally, if x ≽ y ≽ z then ∃ p such that y ∼ px + (1 − p)z.
- Independence: An indifference between two prospects holds also if both prospects are mixed with a
common third prospect or outcome Z: x ≽ y implies (x,p;Z) ≽ (y,p;Z) ∀ p,Z meaning that if you prefer x to y,
adding or mixing another prospect should not change your preference
o If preference satisfy these axioms, then preferences and choice can be modeled using EUT.
o Ask yourself if all your decisions follow each axiom!
Nature of the Standard Model
Formally : According to the standard model, individual i at time t = 0 maximises expected utility subject to a probability
distribution p(s) of the states of the world s ∈ S:
• Individuals maximize expected utility
• An individual's utility is governed by entirely selfish concerns
• Individuals are Bayesian probability operators
• Individuals have consistent time preferences
Attitudes toward risk
- Typical function that has a decreasing marginal utility of money (u(x) = ln(x))
- The expected utility of a prospect will be smaller than the utility of the expectation (utility of the expected
value of the prospect).
Graphically under EUT, concavity of the utility function and risk aversion imply each-other
The utility of a prospect (x,p;y) is given by a linear combination of the utilities of the outcomes, so that it can be found
on the straight-line segment connecting the two utilities.
C: certainty equivalent
Risk premium: RP = EV – c
Certainty Equivalent (CE)
• risk preferences: CE ~ playing the prospect => find the sure amount of money that makes a decision
maker indifferent between playing the prospect and obtaining that amount
• CE: Sure amount equivalent to a gamble’s utility. U(CE) = pU(x) + (1-
p)U(y)
If ce < 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖 : risk averse; EV – ce > 0
If ce = 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖 :risk neutral
If ce > 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖: risk seeking (loving); EV – ce < 0
4
Lecture 1: Introduction
1.1 Wie-wat-waar-wanneer
BE:
• Focuses on understanding why people make economic choices and tests the standard economic model.
Goes == further than the standard model ==, therefore not neglecting it.
• Integrates insights from psychology, experiments, and social sciences.
• Historical roots trace back to Adam Smith, and later economists.
BF:
• Uses insights from psychology to understand how human behaviour influences the decisions of investors,
markets and managers
• Finance: financial markets, corporate finance: individual decision
• Neoclassical economics: individuals and firms are self interested agents who maximize their utility/profits
in the face of constrained resources
• emerged as a reaction to some market anomalies that traditional theory- Efficient Market Hypothesis (EMH)
& models based on investors rationality-failed to explain
Traditional finance: started by center valuations on rational economic behavior.
Rational economic behavior: is modeled through expected utility maximization
Milestones:
• Adam Smith The theory of moral sentiment (1759)
• Vilfredo Pareto (20th century): Economics shifted focus to rational choice models.
• Herbert Simon (21st century): Introduced bounded rationality – limits in thinking capacity, available
information, and time. People often tend to satisfice rather than to maximize
• Daniel Kahneman & Tversky: Identified cognitive biases, framing effects, and reference points
dependence. => assumptions of the standard economic model
• Nobel Prizes:
o 1978: Herbert Simon for decision-making processes within economic organizations.
o 2002: Kahneman for integrating psychology into economics, especially concerning human judgement
and decision-making under uncertainty.
o 2002: Verlon L. Smith for laboratory experiments as a tool in empirical economic analysis: the study of
alternative market mechanisms.
o 2017: Richard Thaler for contributions to behavioral economics.
1.2 Purpose of this course
Learning outcomes:
• Identify paradoxes in standard economic model and (S)EUT
• Apply behavioral concepts to BehavEconApplications|case studies and applications Analyze individual; time and
social preferences. (preferences)
• Use behavioral insights in policy-making nudges.
• Reflect on real-world applications and research opportunities.
1.3 Standard Economic Model
• Neo-classical economic model: way most economist think about consumer welfare & choice, provides a
unified vision of economy based on some common rationality assumptions
• Assumptions: fully rational agents maximize utility purely driven by their self-intrest
• based on:
o Full information: (external knowledge).
o Stable and known preferences: (internal knowledge)
o Optimal decision-making, people chose best option available: (rational choices)
1
,• Information is not generally available; info about existence of information may also not be available where
info is available, people may not obtain it; they may also not be able to process it if they obtain it
• Systematic deviations from the self-interested rational agent model: exist not only for individuals, but also
for firms
• Agents are generally assumed to maximise their utility
• An agent’s utility is governed by purely selfish concerns, in the narrow sense that it does not take into
consideration the utility of other
• Agents are Bayesian probability operators
• Agents have consistent time preferences according to discounted utility model
• Theories are usually normative AND descriptive at the same time; may lead to ==tensions if they fail
descriptively== normative theories (how people should behave to obtain a certain goal: utility maximisation)
descriptive theories (how people actually behave).
Notation:
- Risk: problems in which probabilities are objectively known, observed.
- Uncertainty: when probability are unknown, not observed
o Probability: number between 0 and 1 that indicated a likelihood that particular outcome will occur
0: impossible & 1: certain
Risk for financial assets: assessed differently (variance, downside risk,…)
The probability of all possible events = 1
• Focus: on binary prospects (lotteries) with 2 outcomes x > y and probability p: (x, p ;y) | 0≤p≤1
• Decision trees: P + (1 – p) = 1
=> interested in preference relations between prospects ~ indicates indifference ≻ strict preference (≠ inequality
(>))
Behavioral Critique:
• Satisficing (H. Simon): People often settle for "good enough" solutions.
• Limited information availability and processing capabilities
• Systematic deviations from rationality occur for individuals and firms
1.4 Expected Value Theory
• Expected Value Theory (EVT): value of a prospect: EV(x, p; y) = px + (1-p)y
• EV of a gamble: the value of each possible outcome times the probability of that outcome for i outcomes: 𝐸𝑉(𝑥𝑖,
𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖
Examples:
1. Suppose you are planning to play at an outdoor concert. The probability of rain tomorrow is 0,30, no rain 0,70.
Suppose you will make €500 if it doesn’t rain, but only €100 if it rains.
EV (0,70)(500) + (0,30)(100) = €380
2. You have the option to participate in a game where:
- with a 50% chance (probability p = 0,5) you win €100.
- with a 30% chance (probability p=0,3) you win €50.
- with a 20% chance (probability p=0,2) you win nothing €0.
EV (0,5 x 100) + (0,3 x 50) + (0,2 x 0) = €65
3. Consider a proposed investment action x considered by an investor.
Buying some shares cheaply at initial issue of IPO, then resell them at higher price. Assume one share costs 30p
Assume there are 3 outcomes:
- Share close at flotation cost : 30p → investor gets nothing (0 p payoff)
2
,- Share close at 60p: → investor gets 30p payoff
- Shares close at 90 → investor gets 60p payoff
Outcomes have equal probabilities of happening: p≈0,33
EV (0,33 x 0) + (0,33 x 30) + (0,33 x 60) ≈ 30p (expected value is in term of payoffs not closing prices!)
=> this is a ‘fair bet’: the cost is equal to the expected value
• = Weighted average of possible outcomes: EV = ∑ p_i * x_i
• Limitation of EVT: St. Petersburg paradox: solution infra
1.5 Expected Utility Theory (EUT)
Faced with uncertainty, an individual would maximize the expected utility across possible state of the world
→ applicable to individual/investor decision making
Difficult to apply: asset pricing framework provides a framework to quantity the tradeoff between risk and
return => portfolio framework
St. Petersburg Paradox
• Coin flip game with infinite expected value contradicts real-world willingness to pay.
=> discrepancy between what people seem willing to pay to endter the game & the infinite expected value
• Daniel Bernoulli (1738): Introduced Expected Utility Theory (EUT) as a solution.
• The determination of the value of an item must not be based on the price, but on the utility it yields
𝐸𝑈(𝑥𝑖, 𝑝𝑖)= σ𝑖 𝑝𝑖𝑈(𝑥𝑖)
Where the assumption is that U’(x)>0 and U”(x) < 0 → utility increases in outcomes (money), but at a decreasing rate
Utility:
- Utility refers to satisfaction or pleasure a person derives from consuming a good, service, level of wealth
- We can use it to order person’s preferences over a set of options numerically by assigning larger numerical
values to more preferred options.
- If you prefer eating apples (A) to eating chocolate (C) => U(A) = 2 & U(C) = 1
Choice:
- Under the standard economic model choice is assumed to be revealed preference
- Given the alternatives X and Y if you choose X then this ‘reveals’ that you prefer X to Y (X>Y)
We use utility to explain how individuals make choices to maximize their overall happiness or satisfaction based
on their preferences.
Decreasing marginal utility: utiliy increases as consumption increases but at a diminishing rate
𝜕𝑈
=> quantifies the added satisfaction that an agent gets from consuming additional units of goods or services U’ x =
𝜕𝑋
EUT model
• Focuses on utility (U) instead of monetary Value. EU = ∑ p_i * U (x_i)
• Diminishing marginal utility: => risk aversion; concave utility functions → Bernoulli proposed that
U(x) = ln(x) would solve the St. Petersburg paradox
The expected utility of prospect xi is given by 𝐸𝑈(𝑥𝑖, 𝑝𝑖)= σ𝑖
𝑝𝑖𝑈(𝑥𝑖)
Note the difference with expected value 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖
- EUT: normal model of how a rational agent should make decisions and not a descriptive model of how
decision are made.
When does expected utility work?
EUT is based on 4 main axioms:
3
, - Completeness: a decision maker has defined preference can always decide; x ≽ y or x ≼ y ∀ x,y. (where x,y
could be prospects)
- Transitivity: a decision maker’s preferences are consistent; if x ≽ y and y ≽ z then x ≽ z.
- Continuity: For prospects x, y and z with x ≽ y ≽ z, then there exists a probability p where y is equally good
as px + (1 − p)z
Formally, if x ≽ y ≽ z then ∃ p such that y ∼ px + (1 − p)z.
- Independence: An indifference between two prospects holds also if both prospects are mixed with a
common third prospect or outcome Z: x ≽ y implies (x,p;Z) ≽ (y,p;Z) ∀ p,Z meaning that if you prefer x to y,
adding or mixing another prospect should not change your preference
o If preference satisfy these axioms, then preferences and choice can be modeled using EUT.
o Ask yourself if all your decisions follow each axiom!
Nature of the Standard Model
Formally : According to the standard model, individual i at time t = 0 maximises expected utility subject to a probability
distribution p(s) of the states of the world s ∈ S:
• Individuals maximize expected utility
• An individual's utility is governed by entirely selfish concerns
• Individuals are Bayesian probability operators
• Individuals have consistent time preferences
Attitudes toward risk
- Typical function that has a decreasing marginal utility of money (u(x) = ln(x))
- The expected utility of a prospect will be smaller than the utility of the expectation (utility of the expected
value of the prospect).
Graphically under EUT, concavity of the utility function and risk aversion imply each-other
The utility of a prospect (x,p;y) is given by a linear combination of the utilities of the outcomes, so that it can be found
on the straight-line segment connecting the two utilities.
C: certainty equivalent
Risk premium: RP = EV – c
Certainty Equivalent (CE)
• risk preferences: CE ~ playing the prospect => find the sure amount of money that makes a decision
maker indifferent between playing the prospect and obtaining that amount
• CE: Sure amount equivalent to a gamble’s utility. U(CE) = pU(x) + (1-
p)U(y)
If ce < 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖 : risk averse; EV – ce > 0
If ce = 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖 :risk neutral
If ce > 𝐸𝑉(𝑥𝑖, 𝑝𝑖) = σ𝑖 𝑝𝑖 𝑥𝑖: risk seeking (loving); EV – ce < 0
4
